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Analytics acts as an amplifier for business processes. In business, as in music, “louder” does not always mean “better,” so companies seeking to increase their analytics capacity should keep in mind four principles that underscore its limitations for business.

The Echo Nest, a self-described “music intelligence” company recently acquired by Spotify, uses machine-learning technology to connect people with music. “At our core,” says CEO Jim Lucchese, “what we’re trying to do is what a great deejay does, or the friend that you rely on musically: to better understand who you are as a fan.” In a Q&A, Lucchese describes how the company merges machine learning and cultural analytics to describe music in an analytics-friendly way and help users find new music they’ll enjoy.

What differentiates data scientists from other quantitative analysts? It's partly their skill set and partly their mind set. “The recent emergence of the digital enterprise has created a seemingly insatiable management appetite to amass and analyze data,” write Jeanne G. Harris and Vijay Mehrotra. Data scientists are particularly able to make sense of so much information. For instance, 85% said their projects often or always address new problems, compared to 58% of analysts who made that claim.

Although workers and consumers generate two-thirds of all new data, that’s about to change. Sensors and smart devices — from traffic lights and grocery store scanners to hospital equipment and industrial sensors — are beginning to generate an enormous wave of data that will increase the digital universe ten-fold by 2020. Guest blogger Randy Bean, CEO of NewVantage Partners, explains what this means for executives trying to adapt to a rapidly changing, data-centered business environment.

Data analysts may have external agendas that shape how they address a data set — but Boston College's Sam Ransbotham argues that a savvy manager can identify biases by learning to question the underlying assumptions that go into dataset cleanup and presentation.

When you’re dealing with data on the massive scale that a company like GE uses, a data warehouse just isn’t big enough to house it all. And organizing it ahead of analysis is more of a burden than a help. GE’s CIO Vince Campisi explains to MIT Sloan Management Review why his company is now storing data in a data lake — and how that approach changes the kind of human resources his company is looking for.

Companies will want hundreds of thousands more data scientists than exist, creating a much discussed skills gap. In the past, businesses have figured out how to automate in-demand skills, and some companies say they can automate what data scientists do. What does it mean for companies when they do the equivalent of putting their data scientists into a can?

As data analyses get more complex, how can companies best communicate results to ensure that decision makers have a proper grasp of the data’s implications? Research has found that letting decision makers gain experience on the outcomes of different possible actions by interacting with simulations helps those executives make better decisions. Simulations narrow the often a large gap between what analysts want to share and what decision makers understand, and more clearly illustrate complex statistical information.

In a conversation with MIT Sloan Management Review, Michelle McKenna-Doyle, the NFL's senior vice president and first-ever CIO, discusses the organization’s customer-focused approach to big data and analytics. She explains how the NFL works to make its employees comfortable with their own data sets.

Businesses are running into the issue of having analytics professionals who can’t communicate what they mean. Companies need to train their data scientists to explain how their work helps the business. A little communications 101 is in order, says Meta Brown, whose business has shifted from helping companies analyze data to helping them understand what their analysts are doing.

At the Big Data Innovation Summit, Kaiser’s John Mattison detailed his expectations for the future of health care. He envisions a data-driven system that relies on genetic data in combination with personal data from the patient regarding exposures and lifestyle to help physicians predict health risks. But he also warned that companies have a great deal of work to do to meet the challenges of health care’s digital transformation.

It’s a common assumption: errors and biases in a data set mean the data is useless. Not so fast, says Data & Analytics expert Sam Ransbotham — even data with less-than-great accuracy has its uses, if you understand how to parse it. His blog post explains how to make sense of uncertainty, and how tradeoffs between accuracy and breadth in a data set can better inform your decision-making process.

Some people suggest that the concept of “big data” is nearing the end of its fifteen minutes of fame. They couldn’t be more wrong — because big data isn’t just about managing social media, unstructured data or massive data sets. It is an approach to data and analytics that finds new ways of looking at information — and it’s here to stay.

The way health care is billed in the U.S. system is part of the reason costs are so high. WellPoint, one of the largest providers of health care benefits and insurance in the U.S., is using analytics to change its provider payment system. The goal: promote a health care system based on value, not the volume of services. This Data & Analytics Case Study takes an in-depth look at how WellPoint went from idea to implementation, working with physicians and IT staff to build its Enhanced Personal Health Care program.

ComScore is among the world’s biggest data purveyors — the digital measurement and analytics company has collected about 14 petabytes of online data from around the globe. But it has to execute on all that data internally in order to be successful externally. This is where a lot of organizations stumble. A recent report by MIT Sloan’s Center for Information Systems Research (CISR) details how comScore organizes its assets to capture value from big data.

Using geo-coding and analytics to reshape operations and care is taking hold in healthcare systems. A recent conference on healthcare and geographic information systems from GIS software company Esri highlighted how the Louisiana Department of Health and Hospitals and the Veterans Health Administration use technology to gain better understanding of patient and health trends. The related area of social work is also beginning to see some uptake of the technology.

More than half of managers surveyed strongly agree that their organizations need to step up analytics use, according to a 2013 global survey by MIT Sloan Management Review and SAS Institute. In addition, survey data suggests that in companies where analytics has improved the ability to innovate, managers are more likely to share data with partners and suppliers.

Words have become data; the physical states of our machinery have become data; our physical locations have become data; and even our interactions with each other have become data. Three recent books offer expert perspectives on the increasing power and importance of analytics.

“Big Data in Manufacturing” was the theme of a daylong conference held in Cambridge, Massachusetts, in November 2013 and sponsored by the MIT Forum for Supply Chain Innovation and the Accenture and MIT Alliance in Business Analytics. But the speakers’ insights weren’t restricted to manufacturing.